The effect of TRV027 on coagulation in COVID 19: A pilot randomized, placebo-controlled controlled trial.

COVID-19 causes significant thrombosis and coagulopathy, with elevated D-dimer a predictor of adverse outcome. The precise mechanism of this coagulopathy remains unclear, one hypothesis is that loss of Angiotensin Converting Enzyme 2 activity during viral endocytosis leads to pro-inflammatory angiotensin II accumulation, loss of angiotensin-1-7 and subsequent vascular endothelial activation. We undertook a double blind randomised, placebo controlled experimental medicine study to assess the effect of TRV027, a synthetic angiotensin-1-7 analogue on D-dimer in 30 patients admitted to hospital with COVID-19 (REC ref. 20/HRA/3414), Clinical Trial No. NCT04419610. The study showed a similar rate of adverse events in TRV027 and control groups. There was a numerical decrease in D-dimer in the TRV027 group and increase in D-dimer in the placebo group, however, this did not reach statistical significance (p=0.15). A Bayesian analysis demonstrated there was a 92% probability that this change represented a true drug effect.

Comparative assessment of viral dynamic models for SARS-CoV-2 for pharmacodynamic assessment in early treatment trials.

Pharmacometric analyses of time series viral load data may detect drug effects with greater power than approaches using single time points. Because SARS-CoV-2 viral load rapidly rises and then falls, viral dynamic models have been used. We compared different modelling approaches when analysing Phase II-type viral dynamic data. Using two SARS-CoV-2 datasets of viral load starting within 7 days of symptoms, we fitted the slope-intercept exponential decay (SI), reduced target cell limited (rTCL), target cell limited (TCL) and TCL with eclipse phase (TCLE) models using nlmixr. Model performance was assessed via Bayesian information criterion (BIC), visual predictive checks (VPCs), goodness-of-fit plots, and parameter precision. The most complex (TCLE) model had the highest BIC for both datasets. The estimated viral decline rate was similar for all models except the TCL model for dataset A with a higher rate (median [range] day-1 : dataset A; 0.63 [0.56-1.84]; dataset B: 0.81 [0.74-0.85]). Our findings suggest simple models should be considered during pharmacodynamic model development.

Systematic Review and Patient-Level Meta-Analysis of SARS-CoV-2 Viral Dynamics to Model Response to Antiviral Therapies.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral loads change rapidly following symptom onset, so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient-level meta-analysis of SARS-CoV-2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to date. This systematic review identified case reports, case series, and clinical trial data from publications between January 1, 2020, and May 31, 2020, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A multivariable Cox proportional hazards (Cox-PH) regression model of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed-effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modeling of respiratory viral dynamics was performed to quantify time-dependent drug effects. Patient-level data from 645 individuals (age 1 month to 100 years) with 6,316 viral loads were extracted. Model-based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions, and breast milk were generated. Cox-PH modeling showed longer time to viral clearance in older patients, men, and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, P < 0.001), as well as interferon, particularly when combined with ribavirin (AHR = 2.2, P = 0.015; AHR = 6.04, P = 0.006). Combination therapy should be further investigated. A viral dynamic dataset and NLME model for designing and analyzing antiviral trials has been established.

Tracheostomy care and decannulation during the COVID-19 pandemic. A multidisciplinary clinical practice guideline.

PURPOSE: Traditional critical care dogma regarding the benefits of early tracheostomy during invasive ventilation has had to be revisited due to the risk of COVID-19 to patients and healthcare staff. Standard practises that have evolved to minimise the risks associated with tracheostomy must be comprehensively reviewed in light of the numerous potential episodes for aerosol generating procedures. We meet the urgent need for safe practise standards by presenting the experience of two major London teaching hospitals, and synthesise our findings into an evidence-based guideline for multidisciplinary care of the tracheostomy patient. METHODS: This is a narrative review presenting the extensive experience of over 120 patients with tracheostomy, with a pragmatic analysis of currently available evidence for safe tracheostomy care in COVID-19 patients. RESULTS: Tracheostomy care involves many potentially aerosol generating procedures which may pose a risk of viral transmission to staff and patients. We make a series of recommendations to ameliorate this risk through infection control strategies, equipment modification, and individualised decannulation protocols. In addition, we discuss the multidisciplinary collaboration that is absolutely fundamental to safe and effective practise. CONCLUSION: COVID-19 requires a radical rethink of many tenets of tracheostomy care, and controversy continues to exist regarding the optimal techniques to minimise risk to patients and healthcare workers. Safe practise requires a coordinated multidisciplinary team approach to infection control, weaning and decannulation, with integrated processes for continuous prospective data collection and audit.